
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCamelCase_    :			str    =	""""""
lowerCamelCase_    :			Optional[int]    =	""""""
lowerCamelCase_    :			str    =	""""""
lowerCamelCase_    :			Dict    =	1  # (0 is vertical, 1 is horizontal)







def 							A__       (    )	->						None:
				UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    get_dataset(lowerCamelCase  ,     lowerCamelCase    )
				print("""Processing..."""    )
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    update_image_and_anno(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				for index, image in enumerate(lowerCamelCase    ):
								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								UpperCamelCase_:				Optional[Any]						=    random_chars(32    )
								UpperCamelCase_:				Optional[int]						=    paths[index].split(os.sep    )[-1].rsplit("""."""  ,     1    )[0]
								UpperCamelCase_:				Optional[Any]						=    F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
								cva.imwrite(F'''/{file_root}.jpg'''  ,     lowerCamelCase  ,     [cva.IMWRITE_JPEG_QUALITY, 85]    )
								print(F'''Success {index+1}/{len(lowerCamelCase    )} with {file_name}'''    )
								UpperCamelCase_:				Optional[int]						=    []
								for anno in new_annos[index]:
												UpperCamelCase_:				Tuple						=    F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
												annos_list.append(lowerCamelCase    )
								with open(F'''/{file_root}.txt'''  ,     """w"""    ) as outfile:
												outfile.write("""\n""".join(line for line in annos_list    )    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						tuple[list, list]:
				UpperCamelCase_:				Tuple						=    []
				UpperCamelCase_:				Tuple						=    []
				for label_file in glob.glob(os.path.join(lowerCamelCase  ,     """*.txt"""    )    ):
								UpperCamelCase_:				List[str]						=    label_file.split(os.sep    )[-1].rsplit("""."""  ,     1    )[0]
								with open(lowerCamelCase    ) as in_file:
												UpperCamelCase_:				Any						=    in_file.readlines()
								UpperCamelCase_:				Dict						=    os.path.join(lowerCamelCase  ,     F'''{label_name}.jpg'''    )

								UpperCamelCase_:				Tuple						=    []
								for obj_list in obj_lists:
												UpperCamelCase_:				List[Any]						=    obj_list.rstrip("""\n"""    ).split(""" """    )
												boxes.append(
												    [
												        int(obj[0]    ),
												        float(obj[1]    ),
												        float(obj[2]    ),
												        float(obj[3]    ),
												        float(obj[4]    ),
												    ]    )
								if not boxes:
												continue
								img_paths.append(lowerCamelCase    )
								labels.append(lowerCamelCase    )
				return img_paths, labels







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase = 1    )	->						tuple[list, list, list]:
				UpperCamelCase_:				Optional[int]						=    []
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_:				Tuple						=    []
				for idx in range(len(lowerCamelCase    )    ):
								UpperCamelCase_:				str						=    []
								UpperCamelCase_:				int						=    img_list[idx]
								path_list.append(lowerCamelCase    )
								UpperCamelCase_:				Union[str, Any]						=    anno_list[idx]
								UpperCamelCase_:				Tuple						=    cva.imread(lowerCamelCase    )
								if flip_type == 1:
												UpperCamelCase_:				str						=    cva.flip(lowerCamelCase  ,     lowerCamelCase    )
												for bbox in img_annos:
																UpperCamelCase_:				Dict						=    1 - bbox[1]
																new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
								elif flip_type == 0:
												UpperCamelCase_:				Tuple						=    cva.flip(lowerCamelCase  ,     lowerCamelCase    )
												for bbox in img_annos:
																UpperCamelCase_:				List[str]						=    1 - bbox[2]
																new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]    )
								new_annos_lists.append(lowerCamelCase    )
								new_imgs_list.append(lowerCamelCase    )
				return new_imgs_list, new_annos_lists, path_list







def 							A__       (    lowerCamelCase = 32    )	->						str:
				assert number_char > 1, "The number of character should greater than 1"
				UpperCamelCase_:				Any						=    ascii_lowercase + digits
				return "".join(random.choice(lowerCamelCase    ) for _ in range(lowerCamelCase    )    )


if __name__ == "__main__":
							main()
							print("""DONE ✅""")








import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






